3. Built Environment and Geodemographics Relationships Between Urban Form and Demographics Through various processes- geography of property market, income segregation, household preferences, council housing- strong relationships between housing types, urban form and demographic characteristics. Interesting to explore and map how demographic characteristics relate to urban texture and built environment. Gentrification and Social Change Residential location and urban development dynamic. Processes should be identifiable using this approach.
4. Visualising the Built Environment Can add a sense of place to visualisations, may be useful for public engagement. Also various applications for environmental modelling (energy efficiency, flooding etc). Data Sources Created by combining building outline data (OS Mastermap, Cities Revealed) with LIDAR building heights data (Infoterra, Landmap). Virtual London CASA project creating a 3D building model of Greater London. Based on Mastermap and Infoterra data. Used in various visualisation and modelling projects.
5. London OAC London Demographically Distinct Greater ethnic diversity, income extremes, younger. UK wide OAC classification tends to group all of central London into a small number of super groups. London OAC designed to address this as only applied to Greater London. Developed by Jacob Petersen. 1 Suburban 2 Council Flats 3 Asian Quarters 4 Central District 5 Blue Collar 6 City Commuter 7 London Terraces
6. Case Study: Inner East London Area of contrast and change Historically industrial and working class, ethnically diverse, high immigration and deprivation. Regeneration Massive redevelopment and gentrification over last twenty years, spreading east. Processes continuing. Interesting case study for built environment change and geodemographics.
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12. Building Level Visualisation Pros and Cons Strong Sense of Geography Get a sense of urban texture, river and landmarks. Aids understanding of development and gentrification processes. Visually Engaging May be useful for promotion, public participation. Appropriate Scale Works best at fairly local scales. 2D more appropriate for city wide studies. Spatial Statistical Errors Geodemographic data at Output Area level, while built environment data at building level. Finer scale demographics possible? Useful?
13. New Fine Scale Geography- Address Level Data Innovations Recent improvements to spatial referencing of addresses. From PAF, and related products NLPG and OS AL2. Can be combined with socio-economic data e.g. house price from land registry, valuation office data etc. Advantages Minimise Modifiable Areal Unit Problem with highly disaggregate data. Relate data to real estate properties (e.g. house size), and fine scale locational properties Privacy Concerns Sensitive data often not available, and should be aggregated for publishing. For most applications fine scale not needed, but useful for some, particularly relating to real estate. Technical Challenges Errors in address matching, Computational intensity.
17. Housing Classification Patterns Local Scale Patterns Highly diverse housing types. Likely due to complex history of growth, infill, conversions, local town centres. Implies local demographic diversity also? City Wide Trends Need to aggregate the data. Example uses a 100m grid showing the most frequently occurring dwelling type in each cell. Clear density gradient from the city centre to outskirts. Interplay play of local and city wide influences.
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19. Density gradient from centre, greatest demand where access to jobs highest.Terraced housing the most common dwelling type in Greater London.
20. Conclusions Many relationships between demographics and the built environment that can be explored using this approach. Addition of buildings and building heights can add a sense of place, consider urban texture in demographics. Was useful in mapping gentrification processes in Inner East London. Most useful and practical at local scales. Need to be aware of statistical errors relating to the MAUP. Trends towards an address based geography to minimise MAUP errors. This is most relevant for real estate and housing type applications.
21. Thank you for listening! Welcome comments and questions. Contact Email: duncan.a.smith@ucl.ac.uk More about research: www.casa.ucl.ac.uk , blog.casa.ucl.ac.uk More about urban visualisation: digitalurban.blogspot.com Data providers for this research: Ordnance Survey Infoterra Greater London Authority
